Region-Wise Propagation of Display Parameter Adjustments

The subject disclosure relates generally to medical imaging, and more specifically to region-wise propagation of display parameter adjustments.

A medical image can be electronically displayed or rendered according to a display parameter. Different anatomical regions within the medical image can be more visible or less visible when the display parameter is set to different specific values. Adjusting the display parameter so as to better view different anatomical regions can be laborious or time-consuming.

Accordingly, systems or techniques that can address one or more of these technical problems can be desirable.

The following presents a summary to provide a basic understanding of one or more embodiments. This summary is not intended to identify key or critical elements, or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus or computer program products that facilitate region-wise propagation of display parameter adjustments are described.

According to one or more embodiments, a system is provided. The system can comprise a non-transitory computer-readable memory that can store computer-executable components. The system can further comprise a processor that can be operably coupled to the non-transitory computer-readable memory and that can execute the computer-executable components stored in the non-transitory computer-readable memory. In various embodiments, the computer-executable components can comprise a display component that can display on an electronic screen a medical image depicting a plurality of anatomical regions of a medical patient. In various aspects, the computer-executable components can comprise an adjustment component that can, in response to an instruction requesting an adjustment to a display parameter of a first one of the plurality of anatomical regions, perform the adjustment on the display parameter of the first one of the plurality of anatomical regions and perform propagated versions of the adjustment on the display parameter of remaining ones of the plurality of anatomical regions.

According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method can comprise displaying, by a device operatively coupled to a processor, on an electronic screen a medical image depicting a plurality of anatomical regions of a medical patient. In various aspects, the computer-implemented method can comprise performing, by the device and in response to an instruction requesting an adjustment to a display parameter of a first one of the plurality of anatomical regions, the adjustment on the display parameter of the first one of the plurality of anatomical regions and propagated versions of the adjustment on the display parameter of remaining ones of the plurality of anatomical regions.

According to one or more embodiments, a computer program product for facilitating region-wise propagation of display parameter adjustments is provided. In various embodiments, the computer program product can comprise a non-transitory computer-readable memory having program instructions embodied therewith. In various aspects, the program instructions can be executable by a processor to cause the processor to render a scanned medical image. In various instances, the program instructions can be executable to cause the processor to propagate, in response to an input specifying an adjustment to be made to a rendition parameter of a first anatomical structure depicted in the scanned medical image, a version of the adjustment to the rendition parameter of a second anatomical structure depicted in the scanned medical image.

The following detailed description is merely illustrative and is not intended to limit embodiments or application/uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.

One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.

A medical image can be a pixel array or a voxel array that visually depicts anatomical regions (e.g., organs, tissues, bodily fluids, bodily cavities, other anatomical structures) of a medical patient (e.g., human, animal, or otherwise). Medical images can be electronically captured or generated by any suitable medical imaging equipment (e.g., by computed tomography (CT) scanners, by magnetic resonance imaging (MRI) scanners, by X-ray scanners, by ultrasound scanners, by positron emission tomography (PET) scanners, by nuclear medicine (NM) scanners). After being captured or generated, medical images can undergo any suitable types of image processing techniques (e.g., image reconstruction, multiplanar reconstruction, image classification, image segmentation, image denoising, image regression). For example, anatomical region segmentation or localization can be performed on a medical image, so as to determine which specific pixels or voxels make up or belong to each respective anatomical region that is depicted in the medical image.

In any case, a medical image can be electronically displayed or rendered on a computer screen, such that a medical professional (e.g., physician, nurse, technologist) can visually inspect the medical image for diagnostic or prognostic purposes. The computer screen can facilitate such display or rendition by utilizing a display parameter (e.g., window width, window level) which can govern or otherwise control how (e.g., with what colors or shades) individual pixels or voxels within the medical image are visualized or otherwise visually represented. A given anatomical region within the medical image can be more visible when the display parameter is set to certain specific values and can instead be less visible when the display parameter is instead set to other specific values. Equivalently, at a given value of the display parameter, some anatomical regions (e.g., bones) can have better visibility while other anatomical regions (e.g., lungs) can have worse visibility. Because bad visibility of an anatomical region can negatively or adversely affect diagnosis or prognosis quality, implementation of a single or global value of the display parameter for the entire medical image can be considered as disadvantageous or suboptimal.

Existing techniques attempt to address this visibility problem by implementing region-wise values for the display parameter. Indeed, as mentioned above, anatomical regions can be segmented or otherwise localized within a medical image. Thus, it can be known which individual pixels or voxels of the medical image belong to or make up which anatomical regions of the medical image. Rather than applying a single, global, or universal value of the display parameter to each pixel or voxel of the medical image, existing techniques apply respective or different values of the display parameter to the pixels or voxels of different anatomical regions. For instance, existing techniques can cause the display parameter to take on a first value for whichever pixels or voxels belong to a first anatomical region (e.g., bones) and can cause the display parameter to instead take on a second value for whichever pixels or voxels belong to a second anatomical region (e.g., lungs). By visualizing different anatomical regions using different or respective values of the display parameter, it can be possible for all the anatomical regions in the medical image to be simultaneously displayed or rendered with good or sufficient visibility.

Unfortunately, as the inventors of various embodiments described herein recognized, such existing techniques nevertheless suffer from a technical problem. Specifically, when such existing techniques are implemented, adjustment of the display parameter by a user can become excessively laborious or time-consuming. Indeed, the present inventors recognized that existing techniques treat the display parameter values of whatever anatomical regions are depicted in a medical image as being wholly or completely isolated from, separate from, or independent of each other. For instance, suppose that the pixels or voxels belonging to a first anatomical region in the medical image are visualized using a first value of the display parameter. Furthermore, suppose that a medical professional who is visually inspecting the medical image determines or judges that the visual inspection would be better if the pixels or voxels belonging to the first anatomical region were instead visualized using a second value of the display parameter. In such case, the medical professional can manually adjust or change (e.g., via a human-computer interface device, such as a keyboard or computer mouse) the first value to the second value, thereby changing or modifying how the pixels or voxels that make up the first anatomical region are visualized. When existing techniques are implemented, such manual adjustment leaves totally untouched the values of the display parameter that are implemented for all the remaining anatomical regions in the medical image. It is often the case that the medical professional will want or desire to respectively adjust the display parameter for multiple other anatomical regions in conjunction with the above adjustment for the first anatomical region. Because the medical image can depict tens or dozens of distinct anatomical regions, such existing techniques can require the medical professional to spend up to several minutes manually performing such adjustments one by one (e.g., first manually adjusting the display parameter for depicted bone tissue, then manually adjusting the display parameter for depicted lung tissue, followed by manually adjusting the display parameter for depicted liver tissue). Additionally, such time-consumption can be significantly exacerbated by the fact that the medical professional will often not know a priori what specific value to which to set the display parameter of any individual anatomical region. Instead, for each given anatomical region, the medical professional will often settle upon a new display parameter value only after tinkering or toying with the display parameter of that given anatomical region in trial-and-error fashion (e.g., to see which new value of the display parameter of the given anatomical region yields a best or most desirable visualization of the given anatomical region). Thus, existing techniques can be considered as effort-intensive and disruptive to the visual inspection being performed by the medical professional. In other words, such existing techniques can force the medical professional to spend excessive amounts of time or effort on diagnosing or prognosing whatever medical patient whose anatomical regions are depicted in the medical image. This can be considered as undesirable or disadvantageous.

So, systems or techniques that can address one or more of these technical problems can be desirable.

Various embodiments described herein can address one or more of these technical problems. One or more embodiments described herein can include systems, computer-implemented methods, apparatus, or computer program products that can facilitate region-wise propagation of display parameter adjustments. In particular, a medical image depicting a plurality of anatomical regions can be rendered on a computer screen, and the computer screen can accomplish such rendition via a display parameter that has different values respectively corresponding to the plurality of anatomical regions. A medical professional can provide an electronic instruction to adjust the display parameter for any given anatomical region depicted in the medical image. In response to such electronic instruction, various embodiments described herein can implement the instructed adjustment with respect to the display parameter of the given anatomical region and can also propagate respective versions of the instructed adjustment to the display parameter of the remaining anatomical regions in the medical image. In some cases, such propagation can be facilitated by an adjustment coefficient, which various embodiments described herein can explicitly compute or derive from the electronic instruction. In other cases, such propagation can instead be facilitated via execution of a machine learning model (e.g., a deep learning neural network) that is trained as described later herein. In any of such cases, the display parameter values of all of the anatomical regions can be respectively adjusted, notwithstanding that the medical professional specified an adjustment only form the display parameter value of the single given anatomical region. In other words, various embodiments described herein can be considered as treating the display parameter values of the plurality of anatomical regions in an interdependent or intertwined fashion, rather than in an independent, separate, or isolated fashion. Thus, when the medical professional specifies a display parameter adjustment to any one anatomical region, all the other anatomical regions can be automatically and commensurately adjusted without additional manual tinkering on the part of the medical professional. Thus, a total amount of time or effort expended by the medical professional in arriving at a diagnosis or prognosis based on the medical image can be significantly reduced, as compared to existing techniques.

Various embodiments described herein can be considered as a computerized tool (e.g., any suitable combination of computer-executable hardware or computer-executable software) that can facilitate region-wise propagation of display parameter adjustments. In various aspects, such computerized tool can comprise a display component and an adjustment component.

In various embodiments, there can be a medical image. In various aspects, the medical image can be any suitable two-dimensional pixel array or three-dimensional voxel array generated or captured by any suitable medical imaging modality (e.g., can be a CT scanned image; can be an X-ray scanned image; can be an MRI scanned image) and can depict any suitable anatomical structures or portions thereof of any suitable medical patient. In various instances, the medical image can have undergone any suitable type of anatomical segmentation or localization procedure, such that it is known which specific pixels or voxels of the medical image form or constitute each anatomical structure (e.g., each pixel or voxel can belong to a respective anatomical structure). In other words, each pixel or voxel of the medical image can be considered as having a respective label that indicates or specifies to which anatomical structure that pixel or voxel belongs.

Although it is often the case that each pixel or voxel belongs to exactly one anatomical structure (e.g., has exactly one label), this is a mere non-limiting example. Indeed, in some cases, it can be possible for a pixel or voxel to belong to multiple anatomical structures that are nested or embedded within each other (e.g., such that a pixel or voxel can have multiple labels). For example, a given pixel or voxel might belong to a liver lobe, which is necessarily nested or located inside of a liver. Thus, that given pixel or voxel can have a first label indicating membership to the liver and a second label indicating membership to a liver lobe. As another example, a certain pixel or voxel might belong to a lung artery, which is necessarily nested or located inside of a lung. So, that certain pixel or voxel can have a first label indicating membership to the lung and a second label indicating membership to a lung artery.

In various embodiments, there can be an electronic display. In various aspects, the electronic display can be any suitable type of computerized device which can display or render electronic data in a viewable or readable fashion. For instance, the electronic display can be any suitable type of computer screen, computer monitor, or electronic visual projection device. In various cases, the electronic display can utilize or otherwise be associated with a display parameter. In various aspects, the display parameter can be considered as a configurable or selectable software setting of the electronic display that controls, governs, or otherwise affects how the electronic display visually manifests electronic data. For instance, the display parameter can be window width (e.g., can control how many different Hounsfield unit values are depicted by the electronic display via shades or gradations of grey) or window level (e.g., can control which Hounsfield unit value within the window width is depicted by the electronic display via a central or midpoint shade or gradation of grey).

It can be desired for the medical image to be visually displayed or rendered on the electronic display. In various cases, the computerized tool can accomplish such visual display or rendition as described herein.

In various embodiments, the computerized tool can electronically access the medical image. For instance, the computerized tool can receive, retrieve, or otherwise obtain the medical image from any suitable centralized or decentralized data structures (e.g., graph data structures, relational data structures, hybrid data structures). Thus, any components of the computerized tool can electronically interact with (e.g., read, write, edit, copy, manipulate) the medical image. Likewise, the computerized tool can electronically access the electronic display. For instance, the computerized tool can electronically interface or communicate with (e.g., send electronic commands to, read electronic signals from) the electronic display. So, any components of the computerized tool can electronically interact with (e.g., activate, deactivate, instruct, command) the electronic display.

In various embodiments, the display component of the computerized tool can instruct, command, or otherwise cause the electronic display to visually render the medical image according to various original display parameter values. In various aspects, each of the original display parameter values can be a scalar which can be assigned to or taken on by the display parameter for whatever pixels or voxels make up or form a respective anatomical structure.

As a non-limiting example, suppose that the anatomical structures include bone tissue, lung tissue, and liver tissue, and suppose that the display parameter is window level. In such case, the original display parameter values can include three scalar values: a value A which can correspond to the bone tissue; a value B which can correspond to the lung tissue; and a value C which can correspond to the liver tissue. In such case, the display component can cause the medical image to be visually or visibly rendered on the electronic display, such that: whatever pixels or voxels of the medical image that belong to or make up the bone tissue utilize a window level having the value A; whatever pixels or voxels of the medical image that belong to or make up the lung tissue utilize a window level having the value B; and whatever pixels or voxels of the medical image that belong to or make up the liver tissue utilize a window level having the value C.

Note that the term “original” can be considered as appropriate for the original display parameter values, since the electronic display is initially or originally (e.g., before a user-instructed adjustment) using such display parameter values to visually depict or render the medical image. Furthermore, note that the original display parameter values can be identified, computed, or determined in any suitable fashion (e.g., can be whatever most recent values were used by the electronic display to respectively visualize the plurality of anatomical regions; can be averages of whatever values are empirically determined to be usually or frequently chosen by large swaths of medical professionals to respectively visualize the plurality of anatomical regions).

In various embodiments, the adjustment component of the computerized tool can electronically access (e.g., receive, retrieve, obtain) a requested adjustment to the display parameter. In various aspects, the requested adjustment can be any suitable electronic data that indicates or specifies a target display parameter value which a medical professional or other user associated with the electronic display desires to be implemented for a target anatomical structure within the medical image. For instance, the medical professional or other user can believe that setting the display parameter of the target anatomical structure to the target value will aid or ease visual inspection or interpretation of the medical image. In various cases, the medical professional or other user can supply the requested adjustment using any suitable human-computer interface device associated with the electronic display (e.g., using a computer mouse of the electronic display; using a touchscreen of the electronic display; using a keyboard of the electronic display; using a voice command system of the electronic display). In various aspects, the adjustment component can electronically cause the electronic display to visually render the medical image in accordance with the requested adjustment to the display parameter, and also in accordance with propagated versions of the requested adjustment to the display parameter.

More specifically, the adjustment component can implement the requested adjustment by changing or editing the original display parameter value associated with the target anatomical structure to the target display parameter value. In contrast, for each remaining or untargeted anatomical structure, the adjustment component can implement a respective propagated display parameter adjustment, by changing or editing the original display parameter value associated with that remaining or untargeted anatomical structure to some new display parameter value that is not specified or indicated in the requested adjustment but that is nevertheless based on the requested adjustment.

In some cases, the adjustment component can compute or calculate the new display parameter value by leveraging an adjustment coefficient that is derived from the requested adjustment. Indeed, the adjustment coefficient can be any suitable scalar value whose magnitude can be based on the original display parameter value and on the target display parameter value of the target anatomical structure. For any remaining or untargeted anatomical structure, the new display parameter value of that remaining or untargeted anatomical structure can be obtained by mathematically applying the adjustment coefficient to whatever original display parameter value corresponds to that remaining or untargeted anatomical structure. As a non-limiting example, the adjustment coefficient can be equal to a difference between the target display parameter value and the original display parameter value of the target anatomical structure. In such case, the new display parameter value of the remaining or untargeted anatomical structure can be equal to a sum between the original display parameter value of the remaining or untargeted anatomical structure and the adjustment coefficient. As another non-limiting example, the adjustment coefficient can be equal to a ratio between the target display parameter value and the original display parameter value of the target anatomical structure. In such case, the new display parameter value of the remaining or untargeted anatomical structure can be equal to a product between the original display parameter value of the remaining or untargeted anatomical structure and the adjustment coefficient. In some instances, the adjustment coefficient can be further based on statistical intensity metrics (e.g., mean intensity or percentile intensity) exhibited by whatever pixels or voxels make up the target anatomical structure. As a non-limiting example, the adjustment coefficient can be equal to a quotient achieved by dividing: a difference between the target display parameter value and the original display parameter value of the target anatomical structure; by a difference between a 90-th percentile intensity value and a 10-th percentile intensity value exhibited by the pixels or voxels that make up the target anatomical structure. In such case, the new display parameter value of the remaining anatomical region can be equal to a sum between: the original display parameter value of the remaining or untargeted anatomical structure; and a product between the adjustment coefficient and a difference between a 90-th percentile intensity value and a 10-th percentile intensity value exhibited by the pixels or voxels that make up the remaining or untargeted anatomical structure. In some aspects, the adjustment coefficient can be mathematically applied to (e.g., added to the original display value of, multiplied by the original display parameter value of) the remaining or untargeted anatomical structure in a weighted fashion (e.g., the adjustment coefficient can be multiplicatively scaled by a weight whose magnitude uniquely corresponds to the remaining or untargeted anatomical structure).

In other cases, the adjustment component can compute or calculate the new display parameter value by leveraging a deep learning neural network. Indeed, the deep learning neural network can include any suitable numbers of any suitable types of layers (e.g., input layer, one or more hidden layers, output layer, any of which can be convolutional layers, dense layers, long short-term memory (LSTM) layers, transformer layers, non-linearity layers, pooling layers, batch normalization layers, or padding layers). As another example, the deep learning neural network can include any suitable numbers of neurons in various layers (e.g., different layers can have the same or different numbers of neurons as each other). As yet another example, the deep learning neural network can include any suitable activation functions (e.g., softmax, sigmoid, hyperbolic tangent, rectified linear unit) in various neurons (e.g., different neurons can have the same or different activation functions as each other). As still another example, the deep learning neural network can include any suitable interneuron connections or interlayer connections (e.g., forward connections, skip connections, recurrent connections). Regardless of its internal architecture, the deep learning neural network can be configured to infer or predict what display parameter adjustments should be implemented for various anatomical structures (e.g., bone, lung), when given display parameter adjustments that are known or desired to be implemented for other anatomical structures (e.g., liver). So, in various aspects, the adjustment component can execute the deep learning neural network on: the original display parameter value and the target display parameter value of the target anatomical structure; and on the original display parameter value of the remaining or untargeted anatomical structure; and such execution can cause the deep learning neural network to produce as output the new display parameter value for the remaining or untargeted anatomical structure. In order for such embodiments to function reliably, the deep learning neural network can first undergo training, such as described later herein.

No matter how the adjustment component determines or identifies propagated versions of the requested adjustment, such propagated versions of the requested adjustment can be considered as being substantively commensurate with or otherwise visually harmonious with the requested adjustment. For instance, in some cases, any of the propagated versions of the requested adjustment can be in a same direction as the requested adjustment itself (e.g., if the medical professional or user requests that a window width of bone tissue be increased by a particular amount, then that request can be propagated such that the window width of lung tissue is also increased by a related amount). Indeed, same-direction propagated adjustments can be considered as helping to maintain visual consistency across the different anatomical structures in the medical image. However, in other cases, any of the propagated versions of the requested adjustment can instead be in an opposite direction as the requested adjustment itself (e.g., if the medical professional or user requests that a window width of bone tissue be increased by a particular amount, then that request can be propagated such that the window width of lung tissue is decreased by a related amount). After all, opposite-direction propagated adjustments can be considered as helping to enhance visual emphasis of one or more anatomical structures in the medical image.

In any case, the adjustment component can implement both the requested adjustment and the propagated versions of the requested adjustment. Implementation of the requested adjustment can be considered as enacting on the medical image whatever visual change is desired or wanted by the medical professional or user. In contrast, implementation of the propagated adjustments can be considered as enacting on the medical image visual changes which are not explicitly requested by the medical professional or user but which are nevertheless commensurate with or visually beneficial in view of the requested adjustment. In this way, the medical professional or user need not spend exorbitant amounts of time on sequentially adjusting the display parameter for each anatomical structure depicted in the medical image. Instead, the medical professional or user can simply adjust the display parameter for a single anatomical structure, and various embodiments described herein can automatically propagate (e.g., in same-direction or opposite-direction fashion) such adjustment to the remaining anatomical structures in the medical image. Thus, a total amount of diagnostic or prognostic effort expended by the medical professional or user can be reduced, as compared to situations in which display parameter adjustments are not propagated across anatomical structures (e.g., as compared to situations in which the display parameter values across anatomical structures are wholly or completely independent, isolated, or separate).

Various embodiments described herein can be employed to use hardware or software to solve problems that are highly technical in nature (e.g., to facilitate region-wise propagation of display parameter adjustments), that are not abstract and that cannot be performed as a set of mental acts by a human. Further, some of the processes performed can be performed by a specialized computer (e.g., medical imaging scanners, electronic computer screens, deep learning neural networks) for carrying out defined acts related to medical imaging.

For example, such defined acts can include: displaying, by a device operatively coupled to a processor, on an electronic screen a medical image depicting a plurality of anatomical regions of a medical patient; and performing, by the device and in response to an instruction requesting an adjustment to a display parameter of a first one of the plurality of anatomical regions, the adjustment on the display parameter of the first one of the plurality of anatomical regions and propagated versions of the adjustment on the display parameter of remaining ones of the plurality of anatomical regions. In various aspects, the adjustment and the propagated versions of the adjustment can be in opposite directions. In other aspects, the adjustment and the propagated versions of the adjustment can be in a same direction. In various instances, the propagated versions of the adjustment can be generated via an adjustment coefficient, wherein the adjustment coefficient can be based on a current value of the display parameter of the first one of the plurality of anatomical regions and based on a target value of the display parameter of the first one of the plurality of anatomical regions indicated by the instruction. In various cases, the adjustment coefficient can be further based on a statistical intensity metric exhibited by whatever pixels or voxels make up the first one of the plurality of anatomical regions. In various aspects, the adjustment coefficient can be applied to the display parameter of the remaining ones of the plurality of anatomical regions in a weighted fashion. In various instances, the propagated versions of the adjustment can be generated via execution of a machine learning model on the adjustment indicated by the instruction. In various cases, the medical image can be a computed tomography scanned image, a magnetic resonance imaging scanned image, an ultrasound scanned image, an X-ray scanned image, a positron emission tomography scanned image, or a nuclear medicine scanned image. In various aspects, the display parameter can be a window width or a window level.

Such defined acts are not performed manually by humans. Indeed, neither the human mind nor a human with pen and paper can: visually render a scanned medical image on a computer screen; and electronically propagate, via an adjustment coefficient or via execution of a trained machine learning model, a window width or window level adjustment for one particular anatomical structure shown in the scanned medical image to other anatomical structures shown in the scanned medical image. Indeed, medical images are pixel arrays or voxel arrays that are captured by inherently-computerized, hardware-based scanners (e.g., CT scanners, X-ray scanners, MRI scanners). Such pixel arrays or voxel arrays cannot be created, displayed, or otherwise manipulated by the human mind without computers. Additionally, machine learning models (e.g., deep learning neural networks) are inherently computerized, software-based constructs that cannot be meaningfully trained or executed in any way by the human mind without computers. Furthermore, display parameters such as window width or window level are configurable software settings that control or govern what colors or shades are utilized to visually manifest the individual pixels or voxels of a scanned medical image. Such software settings simply cannot exist outside of a computerized environment. Accordingly, various embodiments described herein are inherently-computerized and cannot be implemented in any sensible, practical, or reasonable way without computers.

Moreover, various embodiments described herein can integrate into a practical application various teachings relating to the field of medical imaging. As described above, different types of anatomical structures can have better visibility or worse visibility at different display parameter values. Thus, using a single, global, or universal display parameter value for an entire medical image would be problematic. To address this issue, existing techniques utilize different or respective display parameter values for different anatomical structures (e.g., pixels or voxels that are labeled as bone tissue can have some particular window width, whereas pixels or voxels that are labeled as lung tissue can have some different window width). However, the present inventors realized that such existing techniques treat display parameter values across different anatomical structures as being wholly or totally independent of each other. Unfortunately, such total display parameter independence across anatomical structures can severely exacerbate time-consumption associated with making display parameter adjustments. For example, a medical professional can subjectively or idiosyncratically decide that visual inspection or interpretation of the medical image would be improved by changing the display parameter values of multiple (e.g., in some cases, dozens of) anatomical structures shown in a medical image. When existing techniques are implemented, the medical professional can be forced to spend on the order of several minutes sequentially tinkering with the display parameter values of those multiple anatomical structures (e.g., first tinkering with bone window width for dozens of seconds, then tinkering with lung window width for dozens of seconds, and then tinkering with liver window width for dozens of seconds, thereby yielding a total of several minutes of display parameter tinkering time). Such excessive time-consumption can be considered as undesirable.

Various embodiments described herein can address one or more of these technical problems. In particular, the present inventors realized that any given adjustment to the display parameter of a single anatomical structure in a medical image can be automatically propagated to the display parameter of any or all other anatomical structures in the medical image. In some cases, such propagation can be analytically performed via an adjustment coefficient (e.g., a weighted or unweighted difference or ratio between new and old values of the display parameter of the single anatomical structure). In other cases, such propagation can instead be performed in a probabilistic or stochastic fashion using a machine learning model that is trained to receive as input a requested adjustment to the display parameter of the single anatomical structure and to predict or infer as output commensurate or related adjustments to the display parameter of any or all other anatomical structures. In any of such situations, various embodiments described herein can be considered as treating display parameter values across different anatomical structures in an interdependent or intertwined fashion, rather than in a wholly independent or isolated fashion. For example, a medical professional can subjectively or idiosyncratically decide that visual inspection or interpretation of a medical image would be improved by changing the display parameter values of multiple (e.g., in some cases, dozens of) anatomical structures shown in a medical image. When various embodiments described herein are implemented, the medical professional need only spend time tinkering with the display parameter value of a single one of those multiple anatomical structures, and such tinkering can be automatically propagated to the display parameter values of the remaining anatomical structures (e.g., the medical professional can tinker with bone window width for dozens of seconds, but such tinkering can be propagated automatically to lung window width and liver window width without additional tinkering or time-expenditure by the medical professional). Thus, a total amount of time or effort spent by the medical professional on inspecting or interpreting the medical image can be reduced. In other words, propagation of display parameter adjustments across anatomical structures can be considered as a software tool that reduces the time required by a medical professional to determine a diagnosis or prognosis from medical image inspection.

Additionally, it must be emphasized how counter-intuitive various embodiments described herein are. As explained above, the animating principle of existing techniques is to treat display parameter values across different anatomical structures as wholly separate, independent, or isolated from each other. After all, such existing techniques were developed in response to prior techniques that applied global or universal display parameter values to entire medical images (e.g., that applied the exact same display parameter value to all pixels or voxels in a medical image, without regard to which anatomical structures those pixels or voxels belonged). Accordingly, various embodiments described herein, which treat display parameter values across different anatomical structures as being interdependent but not identical, would have been considered as backtracking, as a step in the wrong direction, or otherwise as undoing of technological progress. In other words, because the very purpose of existing techniques was to avoid the pitfalls of global display parameters by treating display parameter values of different anatomical structures as being totally independent of each other, eliminating such total independence would have been utterly antithetical to such existing techniques and would thus not even have been entertained as a possibility by those of ordinary skill in the art. Indeed, existing techniques considered display parameter independence across different anatomical structures to be a benefit rather than a bug (e.g., so that the display parameter value of each given anatomical structure can be independently optimized without negatively affecting the display parameter values of other anatomical structures). Thus, making display parameter values interdependent (but not necessarily identical) across anatomical structures would have been considered as diametrically opposed to or counter-productive with respect to existing techniques. Accordingly, various embodiments described herein can be considered as a clever, unusual, or surprising way of rendering medical images on electronic displays, when considered against the backdrop of existing techniques.

For at least these reasons, various embodiments described herein certainly constitute a tangible and concrete technical improvement, technical effect, or technical advantage in the field of medical imaging. Accordingly, such embodiments clearly qualify as useful and practical applications of computers.

Furthermore, various embodiments described herein can control real-world tangible devices based on the disclosed teachings. For example, various embodiments described herein can manipulate a real-world computer screen so to as visually render real-world medical images for visual inspection by real-world medical professionals.

It should be appreciated that the herein figures and description provide non-limiting examples of various embodiments and are not necessarily drawn to scale.

1 FIG. 100 102 104 108 illustrates a block diagram of an example, non-limiting systemthat can facilitate region-wise propagation of display parameter adjustments in accordance with one or more embodiments described herein. As shown, a display propagation systemcan be electronically integrated, via any suitable wired or wireless electronic connections, with a medical imageor with an electronic display.

104 104 104 104 104 104 104 104 104 104 In various embodiments, the medical imagecan exhibit any suitable format, size, or dimensionality. As a non-limiting example, the medical imagecan be an x-by-y array of pixels, for any suitable positive integers x and y. As another non-limiting example, the medical imagecan be an x-by-y-by-z array of voxels, for any suitable positive integers x, y, and z. In various instances, the medical imagecan be captured or otherwise generated by any suitable medical imaging scanner, equipment, or modality. As a non-limiting example, the medical imagecan be captured or generated by an X-ray scanner. As another non-limiting example, the medical imagecan be captured or generated by a CT scanner. As yet another non-limiting example, the medical imagecan be captured or generated by an MRI scanner. As even another non-limiting example, the medical imagecan be captured or generated by an ultrasound scanner. As still another non-limiting example, the medical imagecan be captured or generated by a PET scanner. As another non-limiting example, the medical imagecan be captured or generated by an NM scanner.

104 106 106 106 1 106 106 106 106 106 106 106 n In any case, the medical imagecan visually depict or illustrate a plurality of anatomical regionsof any suitable medical patient. In various aspects, the plurality of anatomical regionscan include n regions for any suitable positive integer n>1: an anatomical region() to an anatomical region(). In various instances, each of the plurality of anatomical regionscan be any suitable distinct or unique type of anatomical, biological, or bodily structure of the medical patient. As a non-limiting example, any of the plurality of anatomical regionscan be any suitable organ (e.g., heart, lung, brain, eye, liver, kidney, intestine) or portion thereof of the medical patient. As another non-limiting example, any of the plurality of anatomical regionscan be any suitable tissue (e.g., adipose tissue, calcified tissue, cancerous tissue) or portion thereof of the medical patient. As yet another non-limiting example, any of the plurality of anatomical regionscan be any suitable bodily fluid (e.g., blood, bile, spinal fluid, water) or portion thereof of the medical patient. As even another non-limiting example, any of the plurality of anatomical regionscan be any suitable body cavity (e.g., abdominal cavity, nasal cavity, ear canal) or portion thereof of the medical patient. As still another non-limiting example, any of the plurality of anatomical regionscan be any suitable medical hardware (e.g., pacemaker, stent, vertebral screw, prosthesis) or portion thereof implanted in or on the medical patient.

104 104 104 106 104 106 104 106 106 106 106 In various instances, the medical imagecan have undergone any suitable image reconstruction techniques, such as filtered back projection. Likewise, in various cases, the medical imagecan have undergone any other suitable pre-processing or post-processing techniques, such as reorientation, denoising, or resolution enhancement. As a non-limiting example, any suitable type of anatomical segmentation or anatomical localization technique can be performed on the medical image, such that respective intra-image positions or locations of the plurality of anatomical regionscan be known. In other words, such anatomical segmentation or localization can reveal or identify which specific pixels or voxels of the medical imagebelong to, make up, form, or otherwise constitute respective ones of the plurality of anatomical regions. In still other words, each pixel or voxel of the medical imagecan be assigned a respective label that indicates or specifies which particular one (if any) of the plurality of anatomical regionsthat pixel or voxels belongs to. In various aspects, the plurality of anatomical regionscan be disjoint, such that no pixel or voxel is assigned to more than one of the plurality of anatomical regions. It should be understood or otherwise appreciated that one anatomical region within the plurality of anatomical regionscan be considered as a background or catch-all region or category.

108 108 108 In various embodiments, the electronic displaycan be any suitable type of computing or computerized device that can visually or visibly render image data, so that such image data can be seen or viewed by a user. As a non-limiting example, the electronic displaycan be any suitable computer screen or computer monitor, such as a liquid crystal display (LCD), a plasma display, a cathode ray tube display, or an organic light emitting diode (OLED) display. As another non-limiting example, the electronic displaycan be any suitable type of computerized image projector which can visually project (e.g., via optical lenses) image data onto a canvas, backstop, or other display surface or display medium (e.g., holographic medium).

108 110 110 108 110 108 In any case, the electronic displaycan have, possess, or otherwise be associated with a display parameter. In various aspects, the display parametercan be any suitable selectable or configurable software setting which can control, govern, dictate, affect, or otherwise influence how the electronic displayvisually or visibly renders image data. In other words, the display parametercan be considered as a variable which can take on scalar values sourced or chosen from any suitable domain within the real numbers (e.g., within R), and the specific scalar value that is assigned to that variable at any given time can control some visually perceptible quality (e.g., colors, shades, brightness, contrast) that the electronic displayuses to render image data.

110 min max lower min upper max min max lower upper min lower upper max lower upper lower upper upper lower lower upper lower upper lower upper upper lower lower upper lower upper lower upper max lower upper min As some non-limiting examples, the display parametercan be window width or window level. Recall that, in various medical contexts, each pixel or voxel that is contained within any given collection of image data can be considered as exhibiting a respective intensity value (also referred to as a respective Hounsfield unit value), and such intensity value can be considered as conveying how dense whatever object that was located at or captured by that pixel or voxel is. For instance, pixels or voxels that represent air often exhibit Hounsfield unit values of about −1000; pixels or voxels that represent fat tissue often exhibit Hounsfield unit values of about −100; pixels or voxels that represent water often exhibit Hounsfield unit values of about 0; pixels or voxels that represent soft tissue often exhibit Hounsfield unit values of about 50; pixels or voxels that represent bone tissue often exhibit Hounsfield unit values of about 400; and pixels or voxels that represent metal often exhibit Hounsfield unit values of about 1000. In various aspects, window width and window level can be considered as software settings that control or govern which specific Hounsfield unit values are visualized by which specific shades of white, grey, or black. More specifically, there can be an absolute range of Hounsfield unit values that spans from a minimum-possible value (which can be denoted as H) to a maximum-possible value (which can be denoted as H). Within this absolute range, a contiguous sub-interval of Hounsfield unit values can be defined that spans from some lower-bound value (which can be denoted as H≥H) to some upper-bound value (which can be denoted as H≤H). In other words, the absolute range can be denoted as [H, H], and the contiguous sub-interval can be denoted as [H, H] where H≤H<H≤H. In various aspects, any pixel or voxel whose Hounsfield unit value is less than Hcan be visually represented by black, any pixel or voxel whose Hounsfield unit value is greater than Hcan be visually represented by white, and any pixel or voxel whose Hounsfield unit value is greater than Hbut less than Hcan be represented by a respective shade of grey (e.g., where Hounsfield unit values closer to Hcan be visually represented by progressively lighter shades of grey, and where Hounsfield unit values closer to Hcan be visually represented by progressively darker shades of grey). In such scenario, window width can be considered as being the width or size of [H, H] (e.g., window width can be determined by subtracting Hfrom H). In contrast, window level can be considered as the midpoint or center of [H, H] (e.g., window level can be determined by taking the arithmetic average of Hand H). Accordingly, increasing window width (e.g., widening [H, H]) can have the effect of decreasing visual contrast within rendered image data, whereas decreasing window width (e.g., narrowing [H, H]) can instead have the effect of increasing visual contrast within rendered image data. Additionally, increasing window level (e.g., shifting the center of [H, H] more toward H) can have the effect of decreasing visual brightness of rendered image data, whereas decreasing window level (e.g., shifting the center of [H, H] more toward H) can instead have the effect of increasing visual brightness of rendered image data.

104 108 112 In any case, it can be desired to visually render the medical imageon the electronic display. In various instances, the display propagation systemcan facilitate such visual rendition as described herein.

102 114 116 114 116 114 114 102 118 120 116 118 120 114 In various embodiments, the display propagation systemcan comprise a processor(e.g., computer processing unit, microprocessor) and a non-transitory computer-readable memorythat is operably or operatively or communicatively connected or coupled to the processor. The non-transitory computer-readable memorycan store computer-executable instructions which, upon execution by the processor, can cause the processoror other components of the display propagation system(e.g., display component, adjustment component) to perform one or more acts. In various embodiments, the non-transitory computer-readable memorycan store computer-executable components (e.g., display component, adjustment component), and the processorcan execute the computer-executable components.

102 108 102 108 108 102 102 108 In various embodiments, the display propagation systemcan electronically access or otherwise electronically communicate in any suitable fashion with the electronic display. Accordingly, the display propagation systemcan electronically transmit any suitable electronic data to the electronic display, and the electronic displaycan likewise electronically transmit any suitable electronic data to the display propagation system. Accordingly, any components of the display propagation systemcan interact with, communicate with, or otherwise manipulate the electronic display.

102 104 102 104 102 104 104 102 104 In various aspects, the display propagation systemcan electronically access the medical image. That is, the display propagation systemcan electronically receive, electronically retrieve, or otherwise electronically obtain the medical image, from any suitable electronic source, database, or computerized workstation. In some situations, the display propagation systemcan receive, retrieve, or obtain the medical imagefrom whatever medical imaging scanner, modality, or equipment generated, created, or captured the medical image. In any case, any components of the display propagation systemcan interact with, control, edit, or otherwise manipulate the medical image.

102 118 118 108 104 110 106 In various embodiments, the display propagation systemcan comprise a display component. In various aspects, the display componentcan, as described herein, cause the electronic displayto visually render the medical imageusing values of the display parameterthat respectively correspond to the plurality of anatomical regions.

102 120 120 106 106 In various embodiments, the display propagation systemcan comprise an adjustment component. In various instances, the adjustment componentcan, as described herein, receive a user-supplied display parameter adjustment for one of the plurality of anatomical regionsand can implement not only that user-supplied display parameter adjustment for that one anatomical region but also propagated versions of that user-supplied display parameter adjustment for remaining ones of the plurality of anatomical regions.

118 120 117 102 117 118 120 117 118 120 118 120 Note that, in various instances, the display componentand the adjustment componentcan collectively be considered as being one or more software componentsof the display propagation system. In various aspects, it should be appreciated that the one or more software componentsare described primarily herein as comprising two components (e.g., the display componentand the adjustment component) for ease of explanation and illustration. However, the one or more software componentsare not limited to being implemented as exactly such two components in every embodiment. Indeed, in some embodiments, the functionalities described herein of such two components can be combined in any suitable fashions, so as to be implemented in or by fewer than two components (e.g., in some cases, a single component can perform all of the functionalities that are described herein with respect to the display componentand the adjustment component). In other embodiments, the functionalities described herein of such two components can instead be distributed, separated, split, or fragmented in any suitable fashions, so as to be implemented in or by more than two components (e.g., two or more components can facilitate the functionalities that are performable by the display component; two or more components can facilitate the functionalities that are performable by the adjustment component).

2 FIG. 200 200 100 202 illustrates a block diagram of an example, non-limiting systemincluding a plurality of original display parameter values that can facilitate region-wise propagation of display parameter adjustments in accordance with one or more embodiments described herein. As shown, the systemcan, in various cases, include the same components as the system, and can further include a plurality of original display parameter values.

118 108 104 202 202 106 106 202 202 1 202 202 108 110 106 202 1 106 1 106 1 108 202 1 110 202 106 106 108 202 110 104 110 n n n n n In various embodiments, the display componentcan electronically instruct, electronically command, or otherwise electronically cause the electronic displayto visually render the medical imageusing the plurality of original display parameter values. In various aspects, the plurality of original display parameter valuescan respectively correspond (e.g., in one-to-one fashion) with the plurality of anatomical regions. Since the plurality of anatomical regionscan have n regions, the plurality of original display parameter valuescan likewise have n values: an original display parameter value() to an original display parameter value(). In various instances, each of the plurality of original display parameter valuescan be a real-valued scalar which the electronic displaycan assign to the display parameterso as to visually render pixels or voxels belonging to a respective one of the plurality of anatomical regions. As a non-limiting example, the original display parameter value() can correspond to the anatomical region(). Thus, for visual rendition of whichever pixels or voxels make up, constitute, or are labeled with the anatomical region(), the electronic displaycan assign the original display parameter value() to the display parameter. As another non-limiting example, the original display parameter value() can correspond to the anatomical region(). So, for visual rendition of whichever pixels or voxels make up, constitute, or are labeled with the anatomical region(), the electronic displaycan assign the original display parameter value() to the display parameter. In other words, different anatomical regions of the medical imagecan be visually rendered using different or respective values of the display parameter(e.g., different anatomical regions can be visualized with different or respective window widths; different anatomical regions can be visualized with different or respective window levels).

118 202 202 106 110 202 1 106 1 110 106 1 202 1 202 108 106 108 106 1 110 108 202 1 In various instances, the display componentcan determine, calculate, or identify the plurality of original display parameter valuesin any suitable fashion. As a non-limiting example, the plurality of original display parameter valuescan be any suitable default values that are statistically or empirically associated with the plurality of anatomical regions(e.g., a study or survey can have revealed that a majority of medical professionals set the display parameterto the original display parameter value() when visually inspecting an organ that matches or is of the same biological type as the anatomical region(); a study or survey can have asked medical professionals what specific value they set the display parameterto when visually inspecting an organ that matches or is of the same biological type as the anatomical region(), and the original display parameter value() can be equal to an average or mean of the medical professionals' responses). As another non-limiting example, the plurality of original display parameter valuescan be whatever values that were most recently implemented by the electronic displayfor visualizing the plurality of anatomical regions(e.g., the electronic displaycan have recently rendered some other medical image that depicted an organ matching or otherwise of the same type as the anatomical region(), and whatever value of the display parameterthat the electronic displayused to visually render that matching or same-type organ can be referred to as the original display parameter value()).

118 108 104 104 In any case, the display componentcan cause the electronic displayto visually render the medical image, such that the medical imagecan be visually inspected or interpreted by a medical professional for diagnostic or prognostic purposes.

3 FIG. 300 300 200 302 304 illustrates a block diagram of an example, non-limiting systemincluding a requested display adjustment and a set of propagated display adjustments that can facilitate region-wise propagation of display parameter adjustments in accordance with one or more embodiments described herein. As shown, the systemcan, in some cases, comprise the same components as the system, and can further comprise a requested display adjustmentor a set of propagated display adjustments.

120 302 302 108 108 108 108 108 302 102 302 104 302 202 104 120 120 202 304 4 8 FIGS.- In various embodiments, the adjustment componentcan electronically access in any suitable fashion (e.g., receive, retrieve, or obtain from any suitable electronic source) the requested display adjustment. In some aspects, the requested display adjustmentcan be provided by a medical professional interacting with any suitable human-computer interface device associated with the electronic display. Non-limiting examples of such human-computer interface device can include: a touchscreen of the electronic display; a keyboard, keypad, or other button panel of the electronic display; a computer mouse or joystick of the electronic display; or a voice command system of the electronic display. In other aspects, the requested display adjustmentcan be provided by any other suitable computerized device that is separate or remote from the display propagation system. As a non-limiting example, the requested display adjustmentcan be provided by an artificially intelligent computerized agent whose task is to analyze or otherwise interact with the medical image. In various instances, the requested display adjustmentcan be any suitable electronic data that specifies, represents, or otherwise conveys that the medical professional desires or wants at least one of the plurality of original display parameter valuesto be changed to some new value (e.g., to enhance or improve visual inspection of the medical imageby the medical professional). In various cases, the adjustment componentcan electronically implement such change. However, the adjustment componentcan also electronically implement a version of such change on at least one remaining, other, or unspecified one of the plurality of original display parameter values, and such other changes can be referred to as the set of propagated display adjustments. Non-limiting aspects are described with respect to.

4 8 FIGS.- 304 302 illustrate example, non-limiting block diagrams showing how the set of propagated display adjustmentscan be created based on the requested display adjustmentin accordance with one or more embodiments described herein.

4 FIG. 302 402 404 402 106 404 110 402 First, consider. In various embodiments, the requested display adjustmentcan contain, include, or specify a target anatomical regionand a target display parameter value. In various aspects, the target anatomical regioncan be any one of the plurality of anatomical regions. In various instances, the target display parameter valuecan be a scalar value which the medical professional desires or wants to be assigned to the display parameterfor visual rendition of whatever pixels or voxels make up the target anatomical region.

106 402 406 406 106 406 406 1 406 202 402 408 202 410 410 410 1 410 410 406 410 1 108 406 1 410 108 406 n n n− n Note that the plurality of anatomical regionscan be considered as being partitioned into the target anatomical regionand a set of untargeted anatomical regions, where the set of untargeted anatomical regionscan be considered as being all the other or remaining ones of the plurality of anatomical regions. Thus, the set of untargeted anatomical regionscan contain a total of n−1 regions: an untargeted anatomical region() to an untargeted anatomical region(−1). In various cases, whichever one of the plurality of original display parameter valuescorresponds to the target anatomical regioncan be referred to as an original display parameter value. In various aspects, all the remaining ones of the plurality of original display parameter valuescan be referred to as a set of original display parameter values. So, the set of original display parameter valuescan contain a total of n−1 values: an original display parameter value() to an original display parameter value(−1). Note that the set of original display parameter valuescan respectively correspond (e.g., in one-to-one fashion) with the set of untargeted anatomical regions(e.g., the original display parameter value() can be whatever value that the electronic displayis currently or presently using to visually render the pixels or voxels that make up the untargeted anatomical region(); the original display parameter value(1) can be whatever value that the electronic displayis currently or presently using to visually render the pixels or voxels that make up the untargeted anatomical region(−1)).

5 FIG. 120 302 120 108 408 404 108 402 408 110 402 404 110 302 402 108 Next, consider. In various embodiments, the adjustment componentcan electronically implement the requested display adjustment. In other words, the adjustment componentcan electronically instruct, electronically command, or otherwise electronically cause the electronic displayto switch or edit the original display parameter valueto the target display parameter value. In still other words, the electronic displaycan be considered as: no longer visually rendering the pixels or voxels that make up the target anatomical regionby assigning the original display parameter valueto the display parameter; and as instead visually rendering the pixels or voxels that make up the target anatomical regionby assigning the target display parameter valueto the display parameter. Such implementation of the requested display adjustmentcan change a visually perceptible quality (e.g., color, brightness, contrast) of the target anatomical regionas visualized by the electronic display.

120 304 304 406 120 302 110 402 110 406 In various aspects, the adjustment componentcan also electronically implement the set of propagated display adjustments. In various instances, the set of propagated display adjustmentscan contain a total of n−1 adjustments respectively corresponding (e.g., in one-to-one fashion) to the set of untargeted anatomical regions. That is, the adjustment componentcan respond to the requested display adjustmentby not only changing the value of the display parameterthat is used for the pixels or voxels that make up the target anatomical region, but also by changing the values of the display parameterthat are used for the pixels or voxels that make up the set of untargeted anatomical regions.

304 120 108 410 1 502 1 108 406 1 410 1 110 406 1 502 1 110 406 1 108 As a non-limiting example, for a first one of the set of propagated display adjustments, the adjustment componentcan electronically instruct, electronically command, or otherwise electronically cause the electronic displayto switch or edit the original display parameter value() to a new display parameter value(). In other words, the electronic displaycan be considered as: no longer visually rendering the pixels or voxels that make up the untargeted anatomical region() by assigning the original display parameter value() to the display parameter; and as instead visually rendering the pixels or voxels that make up the untargeted anatomical region() by assigning the new display parameter value() to the display parameter. This can change a visually perceptible quality (e.g., color, brightness, contrast) of the untargeted anatomical region() as visualized by the electronic display, even though no such change was requested by the medical professional.

304 120 108 410 502 108 406 410 110 406 502 110 406 108 n n n n n n n As another non-limiting example, for an (n−1)-th one of the set of propagated display adjustments, the adjustment componentcan electronically instruct, electronically command, or otherwise electronically cause the electronic displayto switch or edit the original display parameter value(−1) to a new display parameter value(−1). In other words, the electronic displaycan be considered as: no longer visually rendering the pixels or voxels that make up the untargeted anatomical region(−1) by assigning the original display parameter value(−1) to the display parameter; and as instead visually rendering the pixels or voxels that make up the untargeted anatomical region(−1) by assigning the new display parameter value(−1) to the display parameter. This can change a visually perceptible quality (e.g., color, brightness, contrast) of the untargeted anatomical region(−1) as visualized by the electronic display, even though no such change was requested by the medical professional.

502 1 502 502 120 502 302 302 302 n 6 FIG. 7 8 FIGS.- Note that the new display parameter value() to the new display parameter value(−1) can collectively be considered as forming a set of new display parameter values. In various aspects, the adjustment componentcan compute, calculate, or otherwise determine the set of new display parameter valuesbased on the requested display adjustment. In some cases, such computation, calculation, or determination can involve leveraging an adjustment coefficient derived from the requested display adjustment, such as described with respect to. In other cases, such computation, calculation, or determination can involve execution of a trained neural network on the requested display adjustment, such as described with respect to.

6 FIG. 406 406 410 406 410 502 406 502 120 502 602 j j j j j j Now, consider. In various embodiments, any one of the set of untargeted anatomical regionscan be selected. Such selected region can be referred to as an untargeted anatomical region(), for any suitable positive integer j<n. In various aspects, whatever one of the set of original display parameter valuesthat corresponds to the untargeted anatomical region() can be referred to as an original display parameter value(). Likewise, whatever one of the set of new display parameter valuesthat corresponds to the untargeted anatomical region() can be referred to as a new display parameter value(). In various instances, the adjustment componentcan compute or calculate the new display parameter value() by leveraging an adjustment coefficient.

602 408 404 602 404 408 408 404 602 404 408 404 408 In various cases, the adjustment coefficientcan be any suitable real-valued scalar whose magnitude is based on the original display parameter valueand the target display parameter value. As a non-limiting example, the adjustment coefficientcan be equal to an arithmetic difference between the target display parameter valueand the original display parameter value(e.g., can be equal to the difference obtained by subtracting the original display parameter valuefrom the target display parameter value). As another non-limiting example, the adjustment coefficientcan be equal to a ratio between the target display parameter valueand the original display parameter value(e.g., the target display parameter valuecan serve as the numerator of such ratio, and the original display parameter valuecan serve as the denominator of such ratio).

602 604 402 604 402 604 402 604 402 604 402 402 402 602 404 408 604 In some situations, the adjustment coefficientcan be further based on a statistical intensity metricassociated with the target anatomical region. In various aspects, the statistical intensity metriccan be any suitable statistical measurement or composition of statistical measurements that is exhibited by the pixels or voxels that make up the target anatomical region. As a non-limiting example, the statistical intensity metriccan be equal to an average intensity (e.g., average Hounsfield unit value) that is exhibited by the pixels or voxels of the target anatomical region. As another non-limiting example, the statistical intensity metriccan be equal to a median intensity (e.g., median Hounsfield unit value) that is exhibited by the pixels or voxels of the target anatomical region. As still another non-limiting example, the statistical intensity metriccan be equal to an arithmetic difference between any two percentile intensities (e.g., any two percentile Hounsfield unit values) that are exhibited by the pixels or voxels of the target anatomical region(e.g., difference between a 90-th percentile intensity and a 10-th percentile intensity exhibited by the pixels or voxels of the target anatomical region; difference between a 75-th percentile intensity and a 25-th percentile intensity exhibited by the pixels or voxels of the target anatomical region). In any of such cases, the adjustment coefficientcan be obtained by dividing: an arithmetic difference or ratio between the target display parameter valueand the original display parameter value; by the statistical intensity metric.

602 502 602 410 602 404 408 120 602 410 502 602 404 408 120 602 410 502 j j j j j j No matter how the adjustment coefficientis derived, the new display parameter value() can be computed or calculated by mathematically applying (e.g., via any suitable mathematical operation) the adjustment coefficientto the original display parameter value(). As a non-limiting example, suppose that the adjustment coefficientis based on an arithmetic difference between the target display parameter valueand the original display parameter value. In such case, the adjustment componentcan mathematically add the adjustment coefficientto the original display parameter value(), and the result of such addition can be the new display parameter value(). As another non-limiting example, suppose that the adjustment coefficientis based on a ratio between the target display parameter valueand the original display parameter value. In such case, the adjustment componentcan mathematically multiply the adjustment coefficientby the original display parameter value(), and the result of such multiplication can be the new display parameter value().

602 604 120 602 410 606 406 606 604 406 402 604 402 606 406 604 402 606 406 604 402 606 406 120 602 606 602 410 j j j j j j j In situations where the adjustment coefficientis based on the statistical intensity metric, the adjustment componentcan mathematically apply the adjustment coefficientto the original display parameter value() in conjunction with a statistical intensity metricthat corresponds to the untargeted anatomical region(). In various aspects, the statistical intensity metriccan be of the same or identical type as the statistical intensity metric, but can be exhibited by the pixels or voxels of the untargeted anatomical region() rather than by the pixels or voxels of the target anatomical region. As a non-limiting example, suppose that the statistical intensity metricis equal to an average intensity exhibited by the pixels or voxels of the target anatomical region. In such case, the statistical intensity metriccan be equal to whatever average intensity is exhibited by the pixels or voxels of the untargeted anatomical region(). As another non-limiting example, suppose that the statistical intensity metricis equal to a median intensity exhibited by the pixels or voxels of the target anatomical region. In such case, the statistical intensity metriccan be equal to whatever median intensity is exhibited by the pixels or voxels of the untargeted anatomical region(). As even another non-limiting example, suppose that the statistical intensity metricis equal to an arithmetic difference between a 90-th percentile intensity and a 10-th percentile intensity exhibited by the pixels or voxels of the target anatomical region. In such case, the statistical intensity metriccan be equal to an arithmetic difference between a 90-th percentile intensity and a 10-th percentile intensity exhibited by the pixels or voxels of the untargeted anatomical region(). In any of such instances, the adjustment componentcan multiply the adjustment coefficientby the statistical intensity metric, prior to mathematically applying (e.g., prior to adding to or multiplying) the adjustment coefficientto the original display parameter value().

406 608 608 406 402 608 406 402 608 406 402 608 608 608 406 402 120 602 608 602 410 j j j j j j In various aspects, the untargeted anatomical region() can correspond to a weight. In various instances, the weightcan be any suitable real-valued scalar that indicates how much or how little the pixels or voxels of the untargeted anatomical region() should be affected by a visualization change in the pixels or voxels of the target anatomical region(e.g., if the weighthas an absolute value that is greater than 1, this can indicate that the untargeted anatomical region() is or should be more strongly affected by visualization changes in the target anatomical region; conversely, if the weighthas an absolute value that is less than 1, this can indicate that the untargeted anatomical region() is or should be more weakly affected by visualization changes in the target anatomical region). Note that the absolute value or magnitude of the weightcan be chosen, selected, or otherwise determined in any suitable fashion (e.g., the weightcan be established by user-selected preferences; the weightcan be empirically computed by studying or surveying how the medical community usually or normally changes the visualization of the untargeted anatomical region() when also changing the visualization of the target anatomical region). In any case, the adjustment componentcan multiply or scale the adjustment coefficientby the weight, prior to mathematically applying (e.g., prior to adding to or multiplying) the adjustment coefficientto the original display parameter value().

406 610 610 406 402 j j In various aspects, the untargeted anatomical region() can correspond to an adjustment direction indicator. In various instances, the adjustment direction indicatorcan be any suitable electronic data exhibiting any suitable format, size, or dimensionality (e.g., can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more character strings, or any suitable combination thereof) that indicates whether visual rendition of the untargeted anatomical region() should be changed in a same direction as, or instead in a different direction than, visual rendition of the target anatomical region.

610 406 402 120 602 404 408 602 404 408 602 602 608 606 410 404 408 502 410 402 406 404 408 502 410 402 406 402 406 j j j j j j j j j As a non-limiting example, suppose that the adjustment direction indicatorindicates or specifies that display parameter adjustments made to the untargeted anatomical region() should be in a same direction as display parameter adjustments made to the target anatomical region. In such case, the adjustment componentcan add (if the adjustment coefficientis based on an arithmetic difference between the target display parameter valueand the original display parameter value) or multiply (if the adjustment coefficientis based on a ratio between the target display parameter valueand the original display parameter value) the adjustment coefficient(after the adjustment coefficientis scaled or augmented by the weightor the statistical intensity metric, as appropriate) to or by the original display parameter value(). In such case, if the target display parameter valueis greater than the original display parameter value, such addition or multiplication (as appropriate) can cause the new display parameter value() to likewise be greater than the original display parameter value() (e.g., the original display parameter values of the target anatomical regionand the untargeted anatomical region() can have both been increased). Also in such case, if the target display parameter valueis lesser than the original display parameter value, such addition or multiplication (as appropriate) can cause the new display parameter value() to likewise be lesser than the original display parameter value() (e.g., the original display parameter values of the target anatomical regionand the untargeted anatomical region() can have both been decreased). Note that such same-directionality of display parameter adjustments can help to preserve a visual consistency between the target anatomical regionand the untargeted anatomical region(), which can be desirable under some circumstances.

610 406 402 120 602 404 408 602 404 408 602 602 608 606 410 404 408 502 410 402 406 404 408 502 410 402 406 402 406 j j j j j j j j j As another non-limiting example, suppose that the adjustment direction indicatorindicates or specifies that display parameter adjustments made to the untargeted anatomical region() should be in an opposite direction as display parameter adjustments made to the target anatomical region. In such case, the adjustment componentcan subtract (if the adjustment coefficientis based on an arithmetic difference between the target display parameter valueand the original display parameter value) or divide (if the adjustment coefficientis based on a ratio between the target display parameter valueand the original display parameter value) the adjustment coefficient(after the adjustment coefficientis scaled or augmented by the weightor the statistical intensity metric, as appropriate) from or by the original display parameter value(). In such case, if the target display parameter valueis greater than the original display parameter value, such subtraction or division (as appropriate) can cause the new display parameter value() to instead be lesser than the original display parameter value() (e.g., the original display parameter value of the target anatomical regioncan have been increased while that of the untargeted anatomical region() can be decreased). Also in such case, if the target display parameter valueis lesser than the original display parameter value, such subtraction or division (as appropriate) can cause the new display parameter value() to instead be greater than the original display parameter value() (e.g., the original display parameter value of the target anatomical regioncan have been decreased while that of the untargeted anatomical region() can be increased). Note that such opposite-directionality of display parameter adjustments can help to enhance a visual contrast or distinction between the target anatomical regionand the untargeted anatomical region(), which can be desirable under some circumstances.

610 610 610 406 402 j In various aspects, the adjustment direction indicatorcan be chosen, selected, or otherwise determined in any suitable fashion (e.g., the adjustment direction indicatorcan be established by user-selected preferences; the adjustment direction indicatorcan be empirically determined by studying or surveying how the medical community usually or normally changes the visualization of the untargeted anatomical region() when also changing the visualization of the target anatomical region).

6 FIG. 402 406 110 610 604 606 502 j j As a non-limiting illustration of the concepts described with respect to, suppose that the target anatomical regionis liver tissue, the untargeted anatomical region() is lung tissue, the display parameteris window width, the adjustment direction indicatorspecifies same-directionality, and the statistical intensity metricand statistical intensity metricare differences between 90-th and 10-th percentile intensities. In some of such cases, the new display parameter value() can be computed using the following formula:

lung_new lung_original 90_lung 10_lung 502 410 608 606 j j where WWrepresents a new window width for lung tissue (e.g.,()), where WWrepresents an original window width for lung tissue (e.g.,()), where @lung liver represents a weight showing how sensitive display adjustments for lung tissue are with respect to display adjustments for liver tissue (e.g.,), where p−prepresents a difference between a 90-th percentile intensity and a 10-th percentile intensity exhibited by the lung tissue (e.g.,), where

602 404 408 604 502 liver_target liver_original 90_liver 10_liver j represents the adjustment coefficient, where WWrepresents a target window width for liver tissue (e.g.,), where WWrepresents an original window width for liver tissue (e.g.,), and where p−prepresents a difference between a 90-th percentile intensity and a 10-th percentile intensity exhibited by the liver tissue (e.g.,). In others of such cases, the new display parameter value() can be computed using the following formula:

where

402 406 110 610 604 606 502 j j As another non-limiting illustration, suppose that the target anatomical regionis liver tissue, the untargeted anatomical region() is lung tissue, the display parameteris window width, the adjustment direction indicatorspecifies opposite-directionality, and the statistical intensity metricand the statistical intensity metricare differences between 90-th and 10-th percentile intensities. In some of such cases, the new display parameter value() can be computed using the following formula:

where

502 j In others of such cases, the new display parameter value() can be computed using the following formula:

where

120 502 602 6 FIG. In various aspects, the adjustment componentcan compute each of the set of new display parameter valuesutilizing the adjustment coefficientin accordance with(e.g., using respective statistical intensity metrics, respective weights, or respective adjustment direction indicators).

7 FIG. 602 120 502 702 120 702 702 106 106 702 702 1 702 702 702 702 n Next, consider. Rather than utilizing the adjustment coefficient, the adjustment componentcan instead compute the set of new display parameter valuesby leveraging or utilizing a plurality of display propagation neural networks. In particular, the adjustment componentcan electronically store, electronically maintain, electronically control, or otherwise electronically access the plurality of display propagation neural networks. In various aspects, the plurality of display propagation neural networkscan respectively correspond (e.g., in one-to-one fashion) to the plurality of anatomical regions. Since the plurality of anatomical regionscan include a total of n regions, the plurality of display propagation neural networkscan likewise include a total of n networks: a display propagation neural network() to a display propagation neural network(). In various aspects, each of the plurality of display propagation neural networkscan exhibit any suitable deep learning internal architecture. Indeed, in various cases, each of the plurality of display propagation neural networkscan have an input layer, one or more hidden layers, and an output layer. In various instances, any of such layers can be coupled together by any suitable interneuron connections or interlayer connections, such as forward connections, skip connections, or recurrent connections. Furthermore, in various cases, any of such layers can be any suitable types of neural network layers having any suitable learnable or trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer can be convolutional layers, whose learnable or trainable parameters can be convolutional kernels. As another example, any of such input layer, one or more hidden layers, or output layer can be dense layers, whose learnable or trainable parameters can be weight matrices or bias values. As still another example, any of such input layer, one or more hidden layers, or output layer can be batch normalization layers, whose learnable or trainable parameters can be shift factors or scale factors. As even another example, any of such input layer, one or more hidden layers, or output layer can be LSTM layers, whose learnable or trainable parameters can be input-state weight matrices or hidden-state weight matrices. As yet another example, any of such input layer, one or more hidden layers, or output layer can be transformer layers, whose learnable or trainable parameters can be single-head or multi-head attention blocks or other weight matrices. Further still, in various cases, any of such layers can be any suitable types of neural network layers having any suitable fixed or non-trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer can be non-linearity layers, padding layers, pooling layers, or concatenation layers. In various cases, different ones of the plurality of display propagation neural networkscan exhibit the same or different internal architectures as each other.

702 106 106 702 1 106 1 106 1 702 1 106 2 106 702 106 106 702 106 1 106 n n n n n n In various aspects, each of the plurality of display propagation neural networkscan be configured to predict or infer display parameter adjustments for a remainder of the plurality of anatomical regions, when given a display parameter adjustment for a respective one of the plurality of anatomical regions. As a non-limiting example, the display propagation neural network() can correspond to the anatomical region(). So, when given any particular display parameter adjustment that is performed on the pixels or voxels belonging to the anatomical region(), the display propagation neural network() can predict or infer respective display parameter adjustments that should be made to the pixels or voxels of an anatomical region() (not shown) to the anatomical region(). As another non-limiting example, the display propagation neural network() can correspond to the anatomical region(). So, when given any particular display parameter adjustment that is performed on the pixels or voxels belonging to the anatomical region(), the display propagation neural network() can predict or infer respective display parameter adjustments that should be made to the pixels or voxels of the anatomical region() to an anatomical region(−1) (not shown).

702 402 704 120 502 704 120 408 404 410 704 704 704 502 704 406 402 In various embodiments, whichever one of the plurality of display propagation neural networksthat corresponds to the target anatomical regioncan be referred to as a display propagation neural network. In various aspects, the adjustment componentcan compute the set of new display parameter values, by executing the display propagation neural network. For instance, the adjustment componentcan concatenate together the original display parameter value, the target display parameter value, and the set of original display parameter valuesand can feed such concatenation to an input layer of the display propagation neural network. In various cases, such concatenation can complete a forward pass through one or more hidden layers of the display propagation neural network. In various aspects, an output layer of the display propagation neural networkcan calculate the set of new display parameter values, based on hidden activation maps or feature maps produced by the one or more hidden layers during the forward pass. In other words, the display propagation neural networkcan be considered as predicting or inferring how the display parameter value of each of the set of untargeted anatomical regionsshould be increased or decreased, given how much the medical professional desires to increase or decrease the display parameter value of the target anatomical region.

704 8 FIG. In order for the display propagation neural networkto function accurately or reliably, it can first undergo training, such as described with respect to.

8 FIG. 704 Consider, which non-limitingly illustrates how the display propagation neural networkcan be trained in supervised fashion in accordance with various embodiments.

704 120 In various aspects, prior to beginning training, the trainable internal parameters (e.g., convolutional kernels, weight matrices, bias values) of the display propagation neural networkcan be initialized in any suitable fashion (e.g., via random initialization) by the adjustment component.

802 804 802 402 402 406 804 406 802 In various embodiments, there can be a training inputand a ground-truth annotation. In various aspects, the training inputcan be a concatenation of: a training original display parameter value of the target anatomical region; a training target display parameter value of the target anatomical region; and a set of training original display parameter values for the set of untargeted anatomical regions. In various instances, the ground-truth annotationcan be a set of new display parameter values for the set of untargeted anatomical regionsthat are known or deemed to be accurate or correct when given the training input.

120 704 802 704 806 120 802 704 802 704 704 806 In various aspects, the adjustment componentcan execute the display propagation neural networkon the training input, thereby causing the display propagation neural networkto produce an output. More specifically, the adjustment componentcan feed the training inputto the input layer of the display propagation neural network, the training inputcan complete a forward pass through the one or more hidden layers of the display propagation neural network, and the output layer of the display propagation neural networkcan calculate the outputbased on hidden activation maps or feature maps produced by the one or more hidden layers during the forward pass.

806 704 806 704 806 804 806 704 406 802 804 406 802 704 806 806 804 Note that the format, size, or dimensionality of the outputcan be dictated by the number, arrangement, sizes, or other characteristics of the neurons, convolutional kernels, attention blocks, or other internal parameters of the output layer (or of any other layers) of the display propagation neural network. Accordingly, the outputcan be forced to have any desired format, size, or dimensionality, by adding, removing, or otherwise adjusting characteristics of the output layer (or of any other layers) of the display propagation neural network. In particular, the outputcan be forced to have the same format, size, or dimensionality as the ground-truth annotation. Accordingly, the outputcan be considered as the predicted or inferred new display parameter values which the display propagation neural networkbelieves should be used to visualize the set of untargeted anatomical regions, when given the training input. In contrast, the ground-truth annotationcan be considered as the correct or accurate new display parameter values which are known or deemed to be used to visualize the set of untargeted anatomical regionswhen given the training input. Note that, if the display propagation neural networkhas so far undergone no or little training, then the outputcan be highly inaccurate. In other words, the outputcan be very different from the ground-truth annotation.

120 808 806 804 120 704 808 In various aspects, the adjustment componentcan compute an error(e.g., mean absolute error, mean squared error, cross-entropy error) between the outputand the ground-truth annotation. In various instances, the adjustment componentcan incrementally update via backpropagation (e.g., stochastic gradient descent) the trainable internal parameters of the display propagation neural networkbased on the error.

704 406 402 In various cases, such execution-and-update procedure can be repeated for any suitable number of training input-annotation pairs. This can ultimately cause the trainable internal parameters of the display propagation neural networkto become iteratively optimized for accurately propagating to the set of untargeted anatomical regionsdisplay adjustments that are made to the target anatomical region. In various aspects, any suitable training batch sizes, any suitable error/loss functions, or any suitable training termination criteria can be utilized during such training.

704 704 702 704 Although the herein disclosure mainly describes the display propagation neural networkas being trained in supervised fashion, this is a mere non-limiting example for ease of explanation and illustration. In various embodiments, any other suitable training paradigms can be used to train the display propagation neural network, such as unsupervised training or reinforcement learning, any of which may be federated or unfederated. Furthermore, it should be understood or otherwise appreciated that any others of the plurality of display propagation neural networkscan be trained in analogous fashion as the display propagation neural network.

120 302 402 406 406 110 106 120 110 106 104 104 In any case, the adjustment componentcan be considered as propagating respective versions of the requested display adjustment(which the medical professional desires to be implemented for the target anatomical region) to each of the set of untargeted anatomical regions. Accordingly, the medical professional need not spend any time or effort on tinkering or toying with the display parameter values of any of the set of untargeted anatomical regions. In other words, the medical professional can manually adjust the display parameterfor a single one of the plurality of anatomical regions, and the adjustment componentcan automatically apply some version of that manual adjustment to the display parameterfor each remaining one of the plurality of anatomical regions. In this way, a total amount of time or effort spent by the medical professional on adjusting the display parameter values of the medical imagecan be reduced. Stated differently, the medical professional's visual inspection of the medical imagecan be sped up or eased by various embodiments described herein, such that the medical professional can arrive at a diagnosis or prognosis more quickly than would otherwise be possible.

9 16 FIGS.- illustrate example, non-limiting experimental results in accordance with one or more embodiments described herein.

9 FIG. 9 FIG. 902 904 904 illustrates a CT scanned imagedepicting various anatomical regions, including a lung region, a liver region, a heart region, a body region, a bone region, and an aorta region. Each of those distinct regions has its own respective window width value and window level value.also illustrates a CT scanned image. The CT scanned imagewas achieved by manually adjusting the window width and window level of a single one of those distinct regions (e.g., liver). As shown, various embodiments described herein uniquely or respectively propagated such manual adjustment to each remaining region without any additional manual action or effort.

10 FIG. 902 1002 1002 illustrates the CT scanned imageand also a CT scanned image. The CT scanned imagewas achieved by manually adjusting the window width and window level of a different one of those distinct regions (e.g., body). As shown, various embodiments described herein uniquely or respectively propagated such manual adjustment to each remaining region without any additional manual action or effort.

11 FIG. 11 FIG. 1100 1100 1100 1100 1100 1100 1100 shows a CT scanned imagedepicting a lung region, a liver region, a heart region, a body region, a bone region, and an aorta region. Also shown inare the specific window width values (denoted “WW”) and the specific window level values (denoted “WL”) used for each of those regions. In particular, the lung region in the CT scanned imagehas a window width value of 1500 and a window level value of −600; the liver region in the CT scanned imagehas a window width value of 300 and a window level value of 70; the heart region in the CT scanned imagehas a window width value of 350 and a window level value of 40; the body region in the CT scanned imagehas a window width value of 400 and a window level value of 40; the bone region in the CT scanned imagehas a window width value of 700 and a window level value of 200; and the aorta region in the CT scanned imagehas a window width value of 350 and a window level value of 40.

12 FIG. 1200 1100 shows a CT scanned image, which was achieved by various embodiments described herein in response to a manual adjustment of the window width value and window level value of the body region of the CT scanned image. Specifically, the window width value of the body region was changed from 400 to 339. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window width value of the lung region was automatically changed from 1500 to about 1414; the window width value of the liver region was automatically changed from 300 to about 282; the window width value of the heart region was automatically changed from 350 to about 276; the window width value of the bone region was automatically changed from 700 to about 504; and the window width value of the aorta region was automatically changed from 350 to about 313. Additionally, the window level value of the body region was changed from 40 to 32. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window level value of the lung region was automatically changed from −600 to about −611; the window level value of the liver region was automatically changed from 70 to about 68; the window level value of the heart region was automatically changed from 40 to about 30; the window level value of the bone region was automatically changed from 200 to about 174; and the window level value of the aorta region was automatically changed from 40 to about 35. Without various embodiments described herein, it would have taken up to several minutes for a medical professional to sequentially tinker with the window width and window level of each distinct region. However, with various embodiments described here, the medical professional can save time by tinkering with the window width and window level of only a single region (e.g., body, in this non-limiting example), and such tinkering can be automatically propagated to the window widths and window levels of all the remaining regions.

13 FIG. 1300 1100 shows a CT scanned image, which was achieved by various embodiments described herein in response to a manual adjustment of the window width value and window level value of the liver region of the CT scanned image. Specifically, the window width value of the liver region was changed from 300 to 270. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window width value of the lung region was automatically changed from 1500 to about 1353; the window width value of the heart region was automatically changed from 350 to about 223; the window width value of the body region was automatically changed from 400 to about 296; the window width value of the bone region was automatically changed from 700 to about 364; and the window width value of the aorta region was automatically changed from 350 to about 287. Additionally, the window level value of the liver region was changed from 70 to 101. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window level value of the lung region was automatically changed from −600 to about −449; the window level value of the heart region was automatically changed from 40 to about 171; the window level value of the body region was automatically changed from 40 to about 148; the window level value of the bone region was automatically changed from 200 to about 548; and the window level value of the aorta region was automatically changed from 40 to about 106. Without various embodiments described herein, it would have taken up to several minutes for a medical professional to sequentially tinker with the window width and window level of each distinct region. However, with various embodiments described here, the medical professional can save time by tinkering with the window width and window level of only a single region (e.g., liver, in this non-limiting example), and such tinkering can be automatically propagated to the window widths and window levels of all the remaining regions.

14 FIG. 1400 1100 shows a CT scanned image, which was achieved by various embodiments described herein in response to a manual adjustment of the window width value and window level value of the liver region of the CT scanned image. Specifically, the window width value of the liver region was changed from 300 to 141. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window width value of the lung region was automatically changed from 1500 to 1182; the window width value of the heart region was automatically changed from 350 to about 112; the window width value of the body region was automatically changed from 400 to 241; the window width value of the bone region was automatically changed from 700 to about 621; and the window width value of the aorta region was automatically changed from 350 to about 112. Additionally, the window level value of the liver region was changed from 70 to 119. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window level value of the lung region was automatically changed from −600 to about −502; the window level value of the heart region was automatically changed from 40 to about 114; the window level value of the body region was automatically changed from 40 to 89; the window level value of the bone region was automatically changed from 200 to about 225; and the window level value of the aorta region was automatically changed from 40 to about 114. Without various embodiments described herein, it would have taken up to several minutes for a medical professional to sequentially tinker with the window width and window level of each distinct region. However, with various embodiments described here, the medical professional can save time by tinkering with the window width and window level of only a single region (e.g., liver, in this non-limiting example), and such tinkering can be automatically propagated to the window widths and window levels of all the remaining regions.

15 FIG. 1500 1100 shows a CT scanned image, which was achieved by various embodiments described herein in response to a manual adjustment of the window width value and window level value of the liver region of the CT scanned image. Specifically, the window width value of the liver region was changed from 300 to 306. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window width value of the lung region was automatically changed from 1500 to 1512; the window width value of the heart region was automatically changed from 350 to 359; the window width value of the body region was automatically changed from 400 to 406; the window width value of the bone region was automatically changed from 700 to about 703; and the window width value of the aorta region was automatically changed from 350 to about 359. Additionally, the window level value of the liver region was changed from 70 to −8. Various embodiments described herein propagated such change to each of the remaining regions as shown: the window level value of the lung region was automatically changed from −600 to about −756; the window level value of the heart region was automatically changed from 40 to −77; the window level value of the body region was automatically changed from 40 to −38; the window level value of the bone region was automatically changed from 200 to 161; and the window level value of the aorta region was automatically changed from 40 to −77. Without various embodiments described herein, it would have taken up to several minutes for a medical professional to sequentially tinker with the window width and window level of each distinct region. However, with various embodiments described here, the medical professional can save time by tinkering with the window width and window level of only a single region (e.g., liver, in this non-limiting example), and such tinkering can be automatically propagated to the window widths and window levels of all the remaining regions.

16 FIG. 1600 1600 1608 1602 1608 1604 1608 1608 1606 604 402 604 402 604 402 illustrates a screen shotof a reduction to practice of various embodiments described herein. Specifically, the screen shotshows a real-world graphical user interface of a real-world computer program created in accordance with various embodiments described herein. Such graphical user interface includes a medical imagebeing visually rendered, a fieldwhich indicates an alphanumeric identifier of the medical image, a fieldwhich indicates region-wise display parameter values that are currently or presently being used to visually render the medical image(e.g., indicates window width value and window level value for each anatomical region in the medical image), and a fieldwhich indicates different ways by which display parameter adjustment can be propagated across anatomical regions (e.g., a “percentiles3-97” way can involve the statistical intensity metricbeing an arithmetic difference between a 97-th percentile intensity value and a 3-rd percentile intensity value exhibited by the pixels or voxels of the target anatomical region; a “percentiles10-90” way can involve the statistical intensity metricbeing an arithmetic difference between a 90-th percentile intensity value and a 10-th percentile intensity value exhibited by the pixels or voxels of the target anatomical region; a “MinMax” way can involve the statistical intensity metricbeing an arithmetic difference between a maximum intensity value and a minimum intensity value exhibited by the pixels or voxels of the target anatomical region; a “notimplemented” way can involve ceasing or prohibiting propagation across anatomical regions).

17 FIG. 1700 102 1700 illustrates a flow diagram of an example, non-limiting computer-implemented methodthat can facilitate region-wise propagation of display parameter adjustments in accordance with one or more embodiments described herein. In various cases, the display propagation systemcan facilitate the computer-implemented method.

1702 118 114 108 104 106 In various embodiments, actcan include displaying, by a device (e.g., via) operatively coupled to a processor (e.g.,), on an electronic screen (e.g.,) a medical image (e.g.,) depicting a plurality of anatomical regions (e.g.,) of a medical patient.

1704 120 302 110 402 304 In various aspects, actcan include performing, by the device (e.g., via) and in response to an instruction requesting an adjustment (e.g.,) to a display parameter (e.g.,) of a first one (e.g.,) of the plurality of anatomical regions, the adjustment on the display parameter of the first one of the plurality of anatomical regions and propagated versions (e.g.,) of the adjustment on the display parameter of remaining ones of the plurality of anatomical regions.

17 FIG. Although not explicitly shown in, the adjustment and the propagated versions of the adjustment can be in opposite directions or can instead be in a same direction as each other.

17 FIG. 602 408 404 604 608 Although not explicitly shown in, the propagated versions of the adjustment can be generated via an adjustment coefficient (e.g.,), wherein the adjustment coefficient can be based on a current value (e.g.,) of the display parameter of the first one of the plurality of anatomical regions and based on a target value (e.g.,) of the display parameter of the first one of the plurality of anatomical regions indicated by the instruction. In various instances, the adjustment coefficient can be further based on a statistical intensity metric (e.g.,) exhibited by whatever pixels or voxels make up the first one of the plurality of anatomical regions. In some cases, the adjustment coefficient can be applied to the display parameter of the remaining ones of the plurality of anatomical regions in a weighted fashion (e.g., using weights such as).

17 FIG. 704 Although not explicitly shown in, the propagated versions of the adjustment can be generated via execution of a machine learning model (e.g.,) on the adjustment indicated by the instruction.

304 304 302 406 Although the herein disclosure has mainly described various embodiments in which the set of propagated display adjustmentscontain n−1 adjustments (e.g., contain one propagated adjustment per untargeted anatomical region), these are mere non-limiting examples for ease of explanation and illustration. In various cases, the set of propagated display adjustmentscan contain fewer than n−1 adjustments (e.g., the requested display adjustmentcan be not propagated to some of the set of untargeted anatomical structures).

116 114 104 302 110 402 304 406 Various embodiments described herein can be implemented as a computer program product comprising a non-transitory computer-readable memory (e.g.,) having program instructions embodied therewith. In various aspects, the program instructions can be executable by a processor (e.g.,) to cause the processor to: render a scanned medical image (e.g.,); and propagate, in response to an input specifying an adjustment (e.g.,) to be made to a rendition parameter (e.g.,) of a first anatomical structure (e.g.,) depicted in the scanned medical image, a version (e.g., any of) of the adjustment to the rendition parameter of a second anatomical structure (e.g., any of) depicted in the scanned medical image.

In various instances, machine learning algorithms or models can be implemented in any suitable way to facilitate any suitable aspects described herein. To facilitate some of the above-described machine learning aspects of various embodiments, consider the following discussion of artificial intelligence (AI). Various embodiments described herein can employ artificial intelligence to facilitate automating one or more features or functionalities. The components can employ various AI-based schemes for carrying out various embodiments/examples disclosed herein. In order to provide for or aid in the numerous determinations (e.g., determine, ascertain, infer, calculate, predict, prognose, estimate, derive, forecast, detect, compute) described herein, components described herein can examine the entirety or a subset of the data to which it is granted access and can provide for reasoning about or determine states of the system or environment from a set of observations as captured via events or data. Determinations can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The determinations can be probabilistic; that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Determinations can also refer to techniques employed for composing higher-level events from a set of events or data.

Such determinations can result in the construction of new events or actions from a set of observed events or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Components disclosed herein can employ various classification (explicitly trained (e.g., via training data) as well as implicitly trained (e.g., via observing behavior, preferences, historical information, receiving extrinsic information, and so on)) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, and so on) in connection with performing automatic or determined action in connection with the claimed subject matter. Thus, classification schemes or systems can be used to automatically learn and perform a number of functions, actions, or determinations.

1 2 3 4 n A classifier can map an input attribute vector, z=(z, z, z, z, z), to a confidence that the input belongs to a class, as by f(z)=confidence (class). Such classification can employ a probabilistic or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to determinate an action to be automatically performed. A support vector machine (SVM) can be an example of a classifier that can be employed. The SVM operates by finding a hyper-surface in the space of possible inputs, where the hyper-surface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches include, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probabilistic classification models providing different patterns of independence, any of which can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.

18 FIG. 1800 In order to provide additional context for various embodiments described herein,and the following discussion are intended to provide a brief, general description of a suitable computing environmentin which the various embodiments of the embodiment described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules or as a combination of hardware and software.

Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data.

Computer-readable storage media can include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disc (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

18 FIG. 1800 1802 1802 1804 1806 1808 1808 1806 1804 1804 1804 With reference again to, the example environmentfor implementing various embodiments of the aspects described herein includes a computer, the computerincluding a processing unit, a system memoryand a system bus. The system buscouples system components including, but not limited to, the system memoryto the processing unit. The processing unitcan be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit.

1808 1806 1810 1812 1802 1812 The system buscan be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memoryincludes ROMand RAM. A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer, such as during startup. The RAMcan also include a high-speed RAM such as static RAM for caching data.

1802 1814 1816 1816 1820 1822 1822 1814 1802 1814 1800 1814 1814 1816 1820 1808 1824 1826 1828 1824 The computerfurther includes an internal hard disk drive (HDD)(e.g., EIDE, SATA), one or more external storage devices(e.g., a magnetic floppy disk drive (FDD), a memory stick or flash drive reader, a memory card reader, etc.) and a drive, e.g., such as a solid state drive, an optical disk drive, which can read or write from a disk, such as a CD-ROM disc, a DVD, a BD, etc. Alternatively, where a solid state drive is involved, diskwould not be included, unless separate. While the internal HDDis illustrated as located within the computer, the internal HDDcan also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment, a solid state drive (SSD) could be used in addition to, or in place of, an HDD. The HDD, external storage device(s)and drivecan be connected to the system busby an HDD interface, an external storage interfaceand a drive interface, respectively. The interfacefor external drive implementations can include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.

1802 The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.

1812 1830 1832 1834 1836 1812 A number of program modules can be stored in the drives and RAM, including an operating system, one or more application programs, other program modulesand program data. All or portions of the operating system, applications, modules, or data can also be cached in the RAM. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.

1802 1830 1830 1802 1830 1832 1832 1830 1832 18 FIG. Computercan optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system, and the emulated hardware can optionally be different from the hardware illustrated in. In such an embodiment, operating systemcan comprise one virtual machine (VM) of multiple VMs hosted at computer. Furthermore, operating systemcan provide runtime environments, such as the Java runtime environment or the .NET framework, for applications. Runtime environments are consistent execution environments that allow applicationsto run on any operating system that includes the runtime environment. Similarly, operating systemcan support containers, and applicationscan be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application.

1802 1802 Further, computercan be enable with a security module, such as a trusted processing module (TPM). For instance with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer, e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution.

1802 1838 1840 1842 1804 1844 1808 A user can enter commands and information into the computerthrough one or more wired/wireless input devices, e.g., a keyboard, a touch screen, and a pointing device, such as a mouse. Other input devices (not shown) can include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unitthrough an input device interfacethat can be coupled to the system bus, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.

1846 1808 1848 1846 A monitoror other type of display device can be also connected to the system busvia an interface, such as a video adapter. In addition to the monitor, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

1802 1850 1850 1802 1852 1856 The computercan operate in a networked environment using logical connections via wired or wireless communications to one or more remote computers, such as a remote computer(s). The remote computer(s)can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer, although, for purposes of brevity, only a memory/storage deviceis illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN) 1854 or larger networks, e.g., a wide area network (WAN). Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

1802 1854 1858 1858 1854 1858 When used in a LAN networking environment, the computercan be connected to the local networkthrough a wired or wireless communication network interface or adapter. The adaptercan facilitate wired or wireless communication to the LAN, which can also include a wireless access point (AP) disposed thereon for communicating with the adapterin a wireless mode.

1802 1860 1856 1856 1860 1808 1844 1802 1852 When used in a WAN networking environment, the computercan include a modemor can be connected to a communications server on the WANvia other means for establishing communications over the WAN, such as by way of the Internet. The modem, which can be internal or external and a wired or wireless device, can be connected to the system busvia the input device interface. In a networked environment, program modules depicted relative to the computeror portions thereof, can be stored in the remote memory/storage device. It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used.

1802 1816 1802 1854 1856 1858 1860 1802 1826 1858 1860 1826 1802 When used in either a LAN or WAN networking environment, the computercan access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devicesas described above, such as but not limited to a network virtual machine providing one or more aspects of storage or processing of information. Generally, a connection between the computerand a cloud storage system can be established over a LANor WANe.g., by the adapteror modem, respectively. Upon connecting the computerto an associated cloud storage system, the external storage interfacecan, with the aid of the adapteror modem, manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interfacecan be configured to provide access to cloud storage sources as if those sources were physically connected to the computer.

1802 The computercan be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

19 FIG. 1900 1900 1910 1910 1900 1930 1930 1930 1910 1930 1900 1950 1910 1930 1910 1920 1910 1930 1940 1930 is a schematic block diagram of a sample computing environmentwith which the disclosed subject matter can interact. The sample computing environmentincludes one or more client(s). The client(s)can be hardware or software (e.g., threads, processes, computing devices). The sample computing environmentalso includes one or more server(s). The server(s)can also be hardware or software (e.g., threads, processes, computing devices). The serverscan house threads to perform transformations by employing one or more embodiments as described herein, for example. One possible communication between a clientand a servercan be in the form of a data packet adapted to be transmitted between two or more computer processes. The sample computing environmentincludes a communication frameworkthat can be employed to facilitate communications between the client(s)and the server(s). The client(s)are operably connected to one or more client data store(s)that can be employed to store information local to the client(s). Similarly, the server(s)are operably connected to one or more server data store(s)that can be employed to store information local to the servers.

Various embodiments may be a system, a method, an apparatus or a computer program product at any possible technical detail level of integration. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of various embodiments. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium can also include the following: a portable computer diskette, a hard disk, a solid state drive such as M.2 (including non-volatile memory express (NVMe) or serial advanced technology attachment (SATA)), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. Computer readable program instructions for carrying out operations of various embodiments can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform various aspects.

Various aspects are described herein with reference to flowchart illustrations or block diagrams of methods, apparatus (systems), and computer program products according to various embodiments. It will be understood that each block of the flowchart illustrations or block diagrams, and combinations of blocks in the flowchart illustrations or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational acts to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart or block diagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the Figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer or computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that various aspects can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of this disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

As used in this application, the terms “component,” “system,” “platform,” “interface,” and the like, can refer to or can include a computer-related entity or an entity related to an operational machine with one or more specific functionalities. The entities disclosed herein can be either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process or thread of execution and a component can be localized on one computer or distributed between two or more computers. In another example, respective components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor. In such a case, the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, wherein the electronic components can include a processor or other means to execute software or firmware that confers at least in part the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. As used herein, the term “and/or” is intended to have the same meaning as “or.” Moreover, articles “a” and “an” as used in the subject specification and annexed drawings should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. As used herein, the terms “example” or “exemplary” are utilized to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as an “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

The herein disclosure describes non-limiting examples. For ease of description or explanation, various portions of the herein disclosure utilize the term “each,” “every,” or “all” when discussing various examples. Such usages of the term “each,” “every,” or “all” are non-limiting. In other words, when the herein disclosure provides a description that is applied to “each,” “every,” or “all” of some particular object or component, it should be understood that this is a non-limiting example, and it should be further understood that, in various other examples, it can be the case that such description applies to fewer than “each,” “every,” or “all” of that particular object or component.

As it is employed in the subject specification, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Further, processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units. In this disclosure, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component are utilized to refer to “memory components,” entities embodied in a “memory,” or components comprising a memory. It is to be appreciated that memory or memory components described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of illustration, and not limitation, nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM). Volatile memory can include RAM, which can act as external cache memory, for example. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Additionally, the disclosed memory components of systems or computer-implemented methods herein are intended to include, without being limited to including, these and any other suitable types of memory.

What has been described above include mere examples of systems and computer-implemented methods. It is, of course, not possible to describe every conceivable combination of components or computer-implemented methods for purposes of describing this disclosure, but many further combinations and permutations of this disclosure are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.